Recurrence and upstaging rates of T1 high-grade urothelial carcinoma of the bladder on repeat resection in a Canadian, resource-limited, healthcare system
Bibliographic record
Abstract
INTRODUCTION: Non-muscle-invasive bladder cancer is the most expensive malignancy to treat. Current Canadian guidelines recommend repeat transurethral resection of bladder tumour (TURBT) within six weeks after initial resection of T1 high-grade (T1HG) urothelial carcinoma, prior to initiation of intravesical bacillus Calmette-Guerin treatment. This is a burden on operating room usage and adds further cost and risk of complications. Internationally, major cancer centres report significant rates of recurrence and upstaging on repeat resection, however, minimal Canadian data is available. We aimed to determine the rate of recurrence and upstaging in a resource-limited, Canadian healthcare system. METHODS: A retrospective review of patients receiving TURBT between November 2009 and November 2014 was performed. Patients were included if they had all three of the following: a pathological diagnosis of T1HG, adequate muscularis propria present in the specimen, and a repeat resection. RESULTS: We reviewed 3166 patients who underwent TURBT and found 173 to meet our inclusion criteria. The overall recurrence and upstaging rates were 57.2% and 9.2%, respectively. Tumour recurrence and upstaging occurred more often in patients who had repeat resection after 12-24 weeks compared to those patients whose repeat resection occurred within 12 weeks. CONCLUSIONS: Although recurrence rates are similar, we have found upstaging rates to be three- to four-fold lower than those previously reported. Despite this, one in 10 patients will be upstaged, justifying use of this resource within our healthcare system. Finally, timely repeat resection, within 12 weeks appears to be associated with preventing disease progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".